Search Results (59)

Wind and humidity are two very vital climate variables that have received little attention by researchers regarding Uganda. This study sought to close this knowledge gap by exposing the dynamics and relationship of windspeed and humidity in Uganda from 1980 to 2023 as well as predicting the future trends from 2025 to 2040. Using high-resolution gridded windspeed and relative humidity (RH) data for the past and seven downscaled and bias-adjusted global climate models within the coupled model intercomparison project phase 6 framework under two shared socioeconomic pathways (SSPs), SPP245 and SSP585, we employed variability, trend, and correlational analyses to expose the wind–humidity nexus at a monthly scale. The results showed a domination of winds of the calm to gentle breeze category across the country, with a maximum magnitude of 6 knots centered over eastern Lake Victoria and eastern Uganda over the historical period. RH was characterized by high to very high magnitudes, except the northern tips of the country, where RH was low for the historical period. Seasonally, both windspeed and RH demonstrated modest variations, with June–July–August (JJA) and September–October–November (SON) having the highest magnitudes, respectively. Similarly, both variables are forecasted to have significant distribution and magnitude changes. For example, windspeeds will be dominated by decreasing trends, while RH will be dominated by increasing trends. Finally, the correlation analysis revealed a strong negative correlation between windspeeds and RH for both the past and future periods, except for the March–April–May (MAM) and September–October–November (SON) seasons, where positive correlations were observed. These findings have practical applications in agriculture, hydrology, thermal comfort, disaster management, and forecasting, especially in the northern, eastern, and Lake Victoria basin regions. The study recommends further finer-scale research at various atmospheric levels and for prolonged future periods and scenarios. Full article

This review assesses gaps in water quality modeling, emphasizing opportunities to improve next-generation models that are essential for managing water quality and are integral to meeting goals of scientific and management agencies. In particular, this paper identifies gaps in water quality modeling capabilities that, if addressed, could support assessments, projections, and evaluations of management alternatives to support ecosystem health and human beneficial use of water resources. It covers surface water and groundwater quality modeling, dealing with a broad suite of physical, biogeochemical, and anthropogenic drivers. Modeling capabilities for six constituents (or constituent categories) are explored: water temperature, salinity, nutrients, sediment, geogenic constituents, and contaminants of emerging concern. Each constituent was followed through the coupled atmospheric-hydrologic-human system, with prominent modeling gaps described for a diverse array of relevant inputs, processes, and human activities. Commonly identified modeling gaps primarily fall under three types: (1) model gaps, (2) data gaps, and (3) process understanding gaps. In addition to potential solutions for addressing specific individual modeling limitations, some broad approaches (e.g., enhanced data collection and compilation, machine learning, reduced-complexity modeling) are discussed as ways forward for tackling multiple gaps. This gap analysis establishes a framework of diverse approaches that may support improved process representation, scale, and accuracy of models for a wide range of water quality issues. Full article

Understanding of the Coupled Model Intercomparison Project (CMIP) and its research progress and applications is critical to answer scientific questions related to climate change. While numerous scientific papers based on CMIP have been published, there is no quantitative study examining scientific research on climate variability, predictability, and change supported by CMIP. Therefore, the statistical characteristics of CMIP-related publications, including journals, disciplines, co-occurrence and burst detection of keywords, and bibliographic coupling, were analyzed using bibliometric analysis. The results show that research based on CMIP has increased exponentially from 2000 to 2023. About 20% of the research was published in the Journal of Climate and Climate Dynamics. CMIP-related research spanned several disciplines, including meteorology, atmospheric science, geosciences, and environmental sciences. The United States, China, and the United Kingdom ranked top three for CMIP publications. The prominent focus of related research involved the whole climate system, including climate change and variability, climate behavior, the carbon cycle, sea surface temperature, sea ice, modeling, bias correction, simulations, climate sensitivity, extreme events, soil moisture, hydrology, and future change. This study can help relevant scientists better understand the developments and trends of CMIP research, thereby facilitating the use of CMIP data. Full article

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km² (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications. Full article

Lake ice phenology serves as a sensitive indicator of climate change in the lake-rich Northeast China. In this study, the freeze-up date (FUD), break-up date (BUD), and ice cover duration (ICD) of 31 lakes were extracted from a time series of the land water surface temperature (LWST) derived from the combined MOD11A1 and MYD11A1 products for the hydrological years 2001 to 2021. Our analysis showed a high correlation between the ice phenology measures derived by our study and those provided by hydrological records (R² of 0.89) and public datasets (R² > 0.7). There was a notable coherence in lake ice phenology in Northeast China, with a trend in later freeze-up (0.21 days/year) and earlier break-up (0.19 days/year) dates, resulting in shorter ice cover duration (0.50 days/year). The lake ice phenology of freshwater lakes exhibited a faster rate of change compared to saltwater lakes during the period from HY2001 to HY2020. We used redundancy analysis and correlation analysis to study the relationships between the LWST and lake ice phenology with various influencing factors, including lake properties, local climate factors, and atmospheric circulation. Solar radiation, latitude, and air temperature were found to be the primary factors. The FUD was more closely related to lake characteristics, while the BUD was linked to local climate factors. The large-scale oscillations were found to influence the changes in lake ice phenology via the coupled influence of air temperature and precipitation. The Antarctic Oscillation and North Atlantic Oscillation correlate more with LWST in winter, and the Arctic Oscillation correlates more with the ICD. Full article

The extensive implementation of the ‘Grain for Green’ project over the Loess Plateau has improved environmental quality. However, it has resulted in a greater consumption of soil water, and its overall hydrological effects remain highly controversial. Our study utilized a coupled land-atmosphere model to evaluate the effects of vegetation changes resulting from revegetation or reclamation on the hydrology of the Loess Plateau. Revegetation was found to stimulate an increase in precipitation, evapotranspiration, and atmospheric water content. However, the increase in precipitation was insufficient to compensate for soil water loss driven by intensified evapotranspiration, resulting in a decrease in both runoff and soil water content. In contrast to revegetation, reclamation would reduce precipitation, although the reduction was less than the decrease in evapotranspiration. This could lead to an increase in both runoff and soil water content. The results provide an important scientific basis for the hydrological effects of vegetation changes on the Loess Plateau, which is particularly important for guiding current and future revegetation activities toward sustainable ecosystem development and water resources management. Full article

Accurate long–term water resource supply simulation and demand estimation are crucial for effective water resource allocation. This study proposes advanced artificial intelligence (AI)–based models for both long–term water resource supply simulation and demand estimation, specifically focusing on the ShihMen Reservoir in Taiwan. A Long Short–Term Memory (LSTM) network model was developed to simulate daily reservoir inflow. The climate factors from the Taiwan Central Weather Bureau’s one–tiered atmosphere–ocean coupled climate forecast system (TCWB1T1) were downscaled using the K–Nearest Neighbors (KNN) method and integrated with the reservoir inflow model to forecast inflow six months ahead. Additionally, Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) were employed to estimate agricultural and public water demand, integrating both hydrological and socio–economic factors. The models were trained and validated using historical data, with the LSTM model demonstrating a strong ability to capture seasonal variations in inflow patterns and the MLP and GRU models effectively estimating water demand. The results highlight the models’ high accuracy and robustness, offering valuable insights into regional water resource allocation. This research provides a framework for integrating AI–driven models with Decision Support Systems (DSSs) to enhance water resource management, especially in regions vulnerable to climatic variability. Full article

Saudi Arabia is threatened by recurrent flash floods caused by extreme precipitation events. To mitigate the risks associated with these natural disasters, we implemented an advanced nationwide flash flood forecast system, boosting disaster preparedness and response. A noteworthy feature of this system is its national-scale operational approach, providing comprehensive coverage across the entire country. Using cutting-edge technology, the setup incorporates a state-of-the-art, three-component system that couples an atmospheric model with hydrological and hydrodynamic models to enable the prediction of precipitation patterns and their potential impacts on local communities. This paper showcases the system’s effectiveness during an extreme precipitation event that struck Jeddah on 24 November 2022. The event, recorded as the heaviest rainfall in the region’s history, led to widespread flash floods, highlighting the critical need for accurate and timely forecasting. The flash flood forecast system proved to be an effective tool, enabling authorities to issue warnings well before the flooding, allowing residents to take precautionary measures, and allowing emergency responders to mobilize resources effectively. Full article

Vegetation is a natural link between the atmosphere, soil, and water, and it significantly influences hydrological processes in the context of climate change. Under global warming, vegetation greening significantly aggravates the water conflicts between vegetation water use and water resources in water bodies in arid and semiarid regions. This study established an improved eco-hydrological coupled model with related accurately remotely sensed hydrological data (precipitation and soil moisture levels taken every 3 j with multiply verification) on a large spatio-temporal scale to determine the optimal vegetation coverage (M*), which explored the trade-off relationship between the water supply, based on hydrological balance processes, and the water demand, based on vegetation transpiration under the impact of climate change, in a semiarid basin. Results showed that the average annual actual vegetation coverage (M) in the Hailar River Basin from 1982 to 2012 was 0.62, and that the average optimal vegetation coverage (M*) was 0.56. In 67.23% of the region, M* was lower than M, which aggravated the water stress problem in the Hailar River Basin. By identifying the sensitivity of M* to vegetation characteristics and meteorological parameters, relevant suggestions for vegetation-type planting were proposed. Additionally, we also analyzed the dynamic threshold of vegetation under different climatic conditions, and we found that M was lower than M* under only four of the twenty-eight climatic conditions considered (rainfall increase by 10%, 20%, and 30% with no change in temperature, and rainfall increase by 20% with a temperature increase of 1 °C), thereby meeting the system equilibrium state under the condition of sustainable development. This study revealed the dynamic relationship between vegetation and hydrological processes under the effects of climate change and provided reliable recommendations to support vegetation management and ecological restoration in river basins. The remote sensing data help us to extend the model in a semiarid basin due to its accuracy. Full article

The Source Region of the Yellow River (SRYR), renowned as the “Water Tower of the Yellow River”, serves as an important water conservation domain in the upper reaches of the Yellow River, significantly influencing water resources within the basin. Based on the Weather Research and Forecasting (WRF) Model Hydrological modeling system (WRF-Hydro), the key variables of the atmosphere–land–hydrology coupling processes over the SRYR during the 2013 rainy season are analyzed. The investigation involves a comparative analysis between the coupled WRF-Hydro and the standalone WRF simulations, focusing on the hydrological response to the atmosphere. The results reveal the WRF-Hydro model’s proficiency in depicting streamflow variations over the SRYR, yielding Nash Efficiency Coefficient (NSE) values of 0.44 and 0.61 during the calibration and validation periods, respectively. Compared to the standalone WRF simulations, the coupled WRF-Hydro model demonstrates enhanced performance in soil heat flux simulations, reducing the Root Mean Square Error (RMSE) of surface soil temperature by 0.96 K and of soil moisture by 0.01 m³/m³. Furthermore, the coupled model adeptly captures the streamflow variation characteristics with an NSE of 0.33. This underscores the significant potential of the coupled WRF-Hydro model for describing atmosphere–land–hydrology coupling processes in regions characterized by cold climates and intricate topography. Full article

Assessing the impact of climate change is essential for developing water resource management plans, especially in areas facing severe issues regarding ecosystem service degradation. This study assessed the effects of climate change on the hydrological balance using the SWAT (Soil and Water Assessment Tool) hydrological model in the Mixteco River Basin (MRB), Oaxaca, Mexico. Temperature and precipitation were predicted with the projections of global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6); the bias was corrected using CMhyd software, and then the best performing GCM was selected for use in the SWAT model. According to the GCM MPI-ESM1-2-LR, precipitation might decrease by between 83.71 mm and 225.83 mm, while temperature might increase by between 2.57 °C and 4.77 °C, causing a greater atmospheric evaporation demand that might modify the hydrological balance of the MRB. Water yield might decrease by 47.40% and 61.01% under the climate scenarios SP245 and SSP585, respectively. Therefore, adaptation and mitigation measures are needed to offset the adverse impact of climate change in the MRB. Full article

Using an aerosol–climate coupled model, this paper has investigated the changes in distributions of anthropogenic and natural particles due to 4 × CO₂-induced global warming, under the low emission scenario of Representative Concentration Pathway 4.5 (RCP4.5). Special attention is paid to the seasonal variations of aerosol size modes. With rising CO₂ levels, surface warming, and changes in atmospheric circulations and hydrologic cycles are found during both summer (JJA) and winter (DJF). For anthropogenic particles, changes in fine anthropogenic particulate matter (PM_2.5, particles with diameters smaller than 2.5 μm) decrease over high-latitude regions and increase over the tropics in both DJF and JJA. Global mean column concentrations of PM_2.5 decrease by approximately 0.19 mg m⁻², and concentrations of coarse anthropogenic particles (CPM, particles with diameters larger than 2.5 μm) increase by 0.005 mg m⁻² in JJA. Changes in anthropogenic particles in DJF are similar to those in JJA, but the magnitudes of maximum regional changes are much smaller than those in JJA. The coarse anthropogenic particles (CPM, particles with diameters larger than 2.5 μm) increase over northern Africa and the Arabian Peninsula during JJA, whereas changes in anthropogenic CPM during DJF are minimal. During both JJA and DJF, changes in anthropogenic CPM are about two orders of magnitude smaller than those of anthropogenic PM_2.5. Enhanced wet deposition by large-scale precipitation under rising CO₂-induced surface warming is the critical factor affecting changes in anthropogenic particles. For natural particles, the distribution of change in the natural PM_2.5 burden is similar to that of natural CPM, but much larger than natural CPM during each season. Both natural PM_2.5 and CPM burdens increase over northern Africa and the Arabian Peninsula during JJA, but decrease over most of the continental regions during DJF. Changes in surface wind speed, divergence/convergence of surface wind, and precipitation are primary reasons for the variation of natural particles. Full article

Droughts can lead to drought disasters, which have become one of the main natural disasters affecting the development of social economies and ecological environments around the world. Timely and effective drought process simulation and prediction based on atmospheric–hydrological coupling is crucial for drought prevention and resistance. The initial condition (IC) is one source causing uncertainty in drought process simulation and prediction, and the impacts are different with drought duration, basin size and region. Therefore, a quantitative method that measures the uncertainty caused by ICs on the drought process simulation in different climate zones is proposed in this study. In this study, the VIC (Variable Infiltration Capacity) model at a resolution of 0.05°, which is proven as an ideal model to reflect drought processes, was used as the hydrological model to obtain soil moisture. By analyzing the Soil Moisture Anomaly Percentage Index (SMAPI) error characteristics that were simulated based on different ICs, an uncertainty index for drought process simulation was constructed in different climate zones. It was found that with the development of a drought process, the uncertainty converges, and it decreases to within 10% after a drought occurs for 5 to 6 months, while it is less than 5% in the particular basin in a humid region. In climate transition zones, both the uncertainty and its decrease rate are greater than those in humid regions. Climate characteristics, as well as soil types and vegetation types, are fundamental factors that cause differences in drought process simulation and uncertainty changes. The precipitation and temperature distribution more obviously vary spatially and temporally, a greater uncertainty is caused by ICs. This quantitative method reveals the impact of ICs on drought process simulation in different climate regions and provides a basis for the further improvement of drought simulation and prediction based on atmospheric–hydrological coupling. Full article

Precipitation, as one of the main components of the hydrological cycle, is known to be significantly impacted by global climate change. In recent years, the frequency of extreme precipitation has increased, resulting in greater destructiveness. Atmospheric circulation has a significant impact on extreme precipitation in a region. This study aims to investigate the prospective changes in extreme precipitation and their relationship with large-scale atmospheric circulation in the Haihe River Basin. The Haihe River Basin is located in the North China Plain. Mountains and plains can be found in both the eastern and western parts of the study region. The summer seasons experience the most precipitation. The monthly and extreme precipitation (based on daily precipitation) results from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models were evaluated using observed precipitation data, which was utilized as a reference. The CMIP6 models were used to assess future changes in the characteristics of extreme precipitation in the study region. The relationship between extreme precipitation and large-scale atmospheric circulation was also analyzed using historical observation data. Remote sensing results regarding land cover and soil erosion were used to analyze the risks of extreme precipitation and their influences in the study region. According to the results, their multi-model ensembles (MME) and BCC-CSM2-MR models, respectively, outperformed all other CMIP6 models in simulating monthly and extreme (based on daily precipitation) precipitation over the study region. Extreme precipitation demonstrated a rising degree of contribution and future risk under numerous scenarios. The degrees of contribution of R95p and R99p are anticipated to increase in the future. BCC-CSM2-MR predicted that Rx1day and Rx5day would decline in the future. Generally, extreme precipitation increased to a greater degree under SSP585 than under SSP245. Both the El Niño–Southern Oscillation and the Pacific Decadal Oscillation displayed substantial resonance with the extreme precipitation from 1962 to 1980 and around 1995, respectively. This study not only improves our understanding of the occurrence of extreme precipitation, but it also serves as a reference for flood control and waterlogging prevention in the Haihe River Basin. Full article

Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change. Full article